Assessing and communicating data quality in policy-relevant research
The quality of scientific information in policy-relevant fields of research is difficult to assess, and quality control in these important areas is correspondingly difficult to maintain. Frequently there are insufficient high-quality measurements for the presentation of the statistical uncertainty in the numerical estimates that are crucial to policy decisions. A grading system is proposed for numerical estimates, that can deal with the full range of data quality from statistically valid estimates to informed guesses. By analysing the underlying quality of numerical estimates, summarised as spread and grade, it is possible to provide simple rules whereby input data can be coded for quality, and these codings can be carried through arithmetical calculations for assessing the quality of model results. Using the NUSAP (numeral, unit, spread, assessment, pedigree) notational system for this purpose allows the more quantitative and the more qualitative aspects of data uncertainty to be managed separately. By way of example, the system is applied to an ecosystem valuation study that uses several different models and data of widely varying quality to arrive at a single estimate of the economic value of wetlands. The NUSAP approach illustrates the major sources of uncertainty in this study.
Bibliographic Reference: Article: Environmental Management, Vol. 16 (1992) No. 1, pp. 121-131
Record Number: 199210484 / Last updated on: 1994-12-02
Original language: en
Available languages: en